Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis

Recruiting

• Conditions: Gastric Cancer; Intestinal Metaplasia; Atrophic Gastritis

• Registration Number: NCT04840056
• Lead Sponsor: Chinese University of Hong Kong

Brief Summary

The primary objectives of this study are:

* To identify clinical or histological factors associated with gastric cancer development in patients with IM and AG

* To establish a machine learning algorithm for prediction of future gastric cancer risks and individual risk stratification in patient with IM and AG

Detailed Description

This is a two-part retrospective study including a clinical data part and a pathology part. A training cohort will be developed from approximately 70% of included cases. It will be followed by a validation cohort with the remaining cases.

Clinical data will be collected retrospectively using the Clinical Data Analysis and Reporting System (CDARS) and Clinical management System (CMS). A cluster-wide cohort (New Territories East Cluster, NTEC) consisting of patients with history of histologically-proven gastric IM and AG will be identified and included for subsequent analysis. The data collection period for the retrospective data will be 2000-2020.

Histology slides will be retrieved retrospectively when available (within NTEC). Whole slide imaging technique will be utilized for the development of training and validation cohorts with machine learning algorithms in the pathology part.

Study Timeline

• Study First Submitted: 2021-03-23
• Study First Submitted QC: 2021-04-07
• Study First Posted: 2021-04-09
• Study Start: 2021-04-15
• Status Verified: 2024-08
• Last Update Submitted: 2024-08-28
• Last Update Posted: 2024-08-29
• Primary Completion: 2025-12-01
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1300

Inclusion Criteria

• Adults >= 18 years of age
• Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)

Exclusion Criteria

• none

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Gastric cancer and gastric dysplasia	20 years	The primary endpoint is the incidence of gastric cancer (intestinal-type) and gastric dysplasia (low grade and high grade dysplasia).

Secondary Outcome Measures

Name	Time	Method
Overall accuracy of machine learning model	20 years	Overall accuracy of machine learning models will be evaluated
Sensitivity of machine learning model	20 years	Sensitivity of machine learning model will be evaluated
Specificity of machine learning model	20 years	Specificity of machine learning model will be evaluated
Positive predictive value of machine learning model	20 years	Positive predictive value of machine learning model will be evaluated
Negative predictive value of machine learning model	20 years	Negative predictive value of machine learning model will be evaluated
Area under the receiver operating characteristic curve of machine learning model	20 years	Area under the receiver operating characteristic curve of machine learning model will be evaluated

Trial Locations

Locations (1)

Prince of Wales Hospital; 🇭🇰 Shatin, New Territories, Hong Kong